Current machine learning paradigm relies on continuous representations like neural networks, which iteratively adjust parameters to approximate outcomes rather than directly learning the structure of problem. This spreads information across the network, causing issues like information loss and incomprehensibility Building on prior work in environment dynamics modeling, we propose a method that learns visual space in a structured, continual manner. Our approach refines networks to capture the core structure of objects while representing significant subvariants in structure efficiently. We demonstrate this with 2D shape detection, showing incremental learning on MNIST without overwriting knowledge and creating compact, comprehensible representations. These results offer a promising step toward a transparent, continually learning alternative to traditional neural networks for visual processing.

本研究解决了当前机器学习中信息分散导致的信息丢失和难以理解的问题。提出了一种结构化的持续学习方法，通过网络精炼来高效地捕捉对象的核心结构，同时代表重要的子变体。研究表明，该方法在不重写知识的情况下，对MNIST进行逐步学习，展现出创建紧凑且可理解的表征的潜力。

通过网络精炼与关系重构持续学习结构化视觉表征